Smart City Gnosys

Smart city article details

Title	Uav-Enabled Collaborative Secure Data Transmission Via Hybrid-Action Multi-Agent Deep Reinforcement Learning
ID_Doc	59291
Authors	Liu S.; Sun G.; Teng S.; Li J.; Zhang C.; Wang J.; Du H.; Liu Y.
Year	2024
Published	Proceedings - IEEE Global Communications Conference, GLOBECOM
DOI	http://dx.doi.org/10.1109/GLOBECOM52923.2024.10901157
Abstract	With the advancement of smart cities, smart manufacturing, and smart transportation, the Internet of Things (IoT) big data platform operating on wireless networks has emerged as a pivotal sector. In such systems, unmanned aerial vehicles (UAVs) play an indispensable support due to their flexibility and adaptability, but the energy sensitivity and limited communication capabilities pose further challenges. In this paper, we study a UAV-assisted secure communication system, where a UAV-enabled virtual antenna array (UVAA) consisting of multiple UAVs communicates with a remote mobile user (MU) by executing collaborative beamforming (CB), and then an eavesdropper exists for eavesdropping the transmission data from UVAA to MU. Then, a UAV-enabled secure communication optimization problem is formulated to maximize the total secrecy rate between the UVAA and the MU by optimizing the roles, locations and excitation current weights of UAVs. Since the considered scenario is dynamic and the UAVs need to cooperate with each other, we propose a hybrid-action multi-agent deep reinforcement learning (MADRL) algorithm (HMAPPO) to efficiently solve the optimization problem. Simulation results verify the effectiveness of the HMAPPO and illustrate that it learns the best strategy compared with other baseline methods. © 2024 IEEE.
Author Keywords	big data collection; collaborative beamforming; multi-agent deep reinforcement learning; secure communications; Unmanned aerial vehicle

Similar Articles

Id	Similarity	Authors	Title	Published
16152	0.891	Kim J.; Park S.; Jung S.; Cordeiro C.	Cooperative Multi-Uav Positioning For Aerial Internet Service Management: A Multi-Agent Deep Reinforcement Learning Approach	IEEE Transactions on Network and Service Management, 21, 4 (2024)
47814	0.889	Fu F.; Jiao Q.; Yu F.R.; Zhang Z.; Du J.	Securing Uav-To-Vehicle Communications: A Curiosity-Driven Deep Q-Learning Network (C-Dqn) Approach	2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings (2021)
45120	0.867	Moudoud H.; El Houda Z.A.; Brik B.	Reputation-Aware Scheduling For Secure Internet Of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach	IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2024 (2024)
44892	0.866	Ul Abideen Tariq Z.; Baccour E.; Erbad A.; Hamdi M.	Reinforcement Learning-Based Anti-Jamming Solution For Aerial Ris-Aided Dense Dynamic Multi-User Environments	20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024 (2024)
43159	0.865	Ahmed U.; Lin J.C.-W.; Srivastava G.	Privacy-Preserving Active Learning On The Internet Of 5G Connected Artificial Intelligence Of Things	IEEE Internet of Things Magazine, 5, 1 (2022)
38095	0.861	Dhuheir M.; Erbad A.; Hamdaoui B.; Belhaouari S.B.; Guizani M.; Vu T.X.	Multi-Agent Meta Reinforcement Learning For Reliable And Low-Latency Distributed Inference In Resource-Constrained Uav Swarms	IEEE Access, 13 (2025)
38480	0.86	Vishnoi V.; Budhiraja I.; Garg D.; Garg S.; Choi B.J.; Hossain M.S.	Multiagent Deep Reinforcement Learning Based Energy Efficient Resource Management Scheme For Ris Assisted D2D Users In 6G-Aided Smart Cities Environment	Alexandria Engineering Journal, 116 (2025)
23748	0.857	Gomes D.; Hasan M.; Philip S.R.	Enhancing Capabilities And Security Features Of Drone Networks Through Machine Learning: A Comprehensive Overview	Advances in Science, Technology and Innovation, Part F372 (2025)
44886	0.855	Tariq Z.U.A.; Baccour E.; Erbad A.; Hamdi M.	Reinforcement Learning For Resilient Aerial-Irs Assisted Wireless Communications Networks In The Presence Of Multiple Jammers	IEEE Open Journal of the Communications Society, 5 (2024)
46706	0.854	Tariq Z.U.A.; Baccour E.; Erbad A.; Hamdi M.; Guizani M.	Rl-Based Adaptive Uav Swarm Formation And Clustering For Secure 6G Wireless Communications In Dynamic Dense Environments	IEEE Access, 12 (2024)